Overview

Dataset statistics

Number of variables17
Number of observations2111
Missing cells0
Missing cells (%)0.0%
Duplicate rows9
Duplicate rows (%)0.4%
Total size in memory280.5 KiB
Average record size in memory136.1 B

Variable types

Categorical5
Numeric8
Boolean4

Alerts

Dataset has 9 (0.4%) duplicate rowsDuplicates
Gender is highly correlated with Height and 2 other fieldsHigh correlation
family_history_with_overweight is highly correlated with Weight and 2 other fieldsHigh correlation
NObeyesdad is highly correlated with Gender and 4 other fieldsHigh correlation
Age is highly correlated with Weight and 2 other fieldsHigh correlation
Height is highly correlated with Gender and 1 other fieldsHigh correlation
Weight is highly correlated with Gender and 7 other fieldsHigh correlation
FCVC is highly correlated with NObeyesdadHigh correlation
CAEC is highly correlated with family_history_with_overweightHigh correlation
CH2O is highly correlated with WeightHigh correlation
FAF is highly correlated with WeightHigh correlation
TUE is highly correlated with WeightHigh correlation
MTRANS is highly correlated with AgeHigh correlation
FAF has 411 (19.5%) zeros Zeros
TUE has 557 (26.4%) zeros Zeros

Reproduction

Analysis started2023-05-26 04:30:42.707247
Analysis finished2023-05-26 04:31:06.028758
Duration23.32 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Gender
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Male
1068 
Female
1043 

Length

Max length6
Median length4
Mean length4.988157271
Min length4

Characters and Unicode

Total characters10530
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male1068
50.6%
Female1043
49.4%

Length

2023-05-26T10:01:06.210273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-05-26T10:01:06.452615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
male1068
50.6%
female1043
49.4%

Most occurring characters

ValueCountFrequency (%)
e3154
30.0%
a2111
20.0%
l2111
20.0%
M1068
 
10.1%
F1043
 
9.9%
m1043
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8419
80.0%
Uppercase Letter2111
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3154
37.5%
a2111
25.1%
l2111
25.1%
m1043
 
12.4%
Uppercase Letter
ValueCountFrequency (%)
M1068
50.6%
F1043
49.4%

Most occurring scripts

ValueCountFrequency (%)
Latin10530
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3154
30.0%
a2111
20.0%
l2111
20.0%
M1068
 
10.1%
F1043
 
9.9%
m1043
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3154
30.0%
a2111
20.0%
l2111
20.0%
M1068
 
10.1%
F1043
 
9.9%
m1043
 
9.9%

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1402
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.31259991
Minimum14
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2023-05-26T10:01:06.663546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17.8914285
Q119.947192
median22.77789
Q326
95-th percentile38.09807
Maximum61
Range47
Interquartile range (IQR)6.052808

Descriptive statistics

Standard deviation6.345968274
Coefficient of variation (CV)0.2610156173
Kurtosis2.826389029
Mean24.31259991
Median Absolute Deviation (MAD)3.22211
Skewness1.529100354
Sum51323.89841
Variance40.27131333
MonotonicityNot monotonic
2023-05-26T10:01:06.906273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18128
 
6.1%
26101
 
4.8%
2196
 
4.5%
2389
 
4.2%
1959
 
2.8%
2048
 
2.3%
2239
 
1.8%
1730
 
1.4%
2418
 
0.9%
2516
 
0.8%
Other values (1392)1487
70.4%
ValueCountFrequency (%)
141
 
< 0.1%
151
 
< 0.1%
169
0.4%
16.0932341
 
< 0.1%
16.1292791
 
< 0.1%
16.1729921
 
< 0.1%
16.1981531
 
< 0.1%
16.2405761
 
< 0.1%
16.2704341
 
< 0.1%
16.306872
 
0.1%
ValueCountFrequency (%)
611
< 0.1%
561
< 0.1%
55.246251
< 0.1%
55.1378811
< 0.1%
55.0224941
< 0.1%
552
0.1%
521
< 0.1%
511
< 0.1%
50.8325591
< 0.1%
47.70611
< 0.1%

Height
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1574
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.701677353
Minimum1.45
Maximum1.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2023-05-26T10:01:07.142538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile1.5482905
Q11.63
median1.700499
Q31.768464
95-th percentile1.85
Maximum1.98
Range0.53
Interquartile range (IQR)0.138464

Descriptive statistics

Standard deviation0.09330481987
Coefficient of variation (CV)0.0548310875
Kurtosis-0.5629488933
Mean1.701677353
Median Absolute Deviation (MAD)0.069769
Skewness-0.01285464646
Sum3592.240893
Variance0.008705789411
MonotonicityNot monotonic
2023-05-26T10:01:07.420823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.760
 
2.8%
1.6550
 
2.4%
1.643
 
2.0%
1.7539
 
1.8%
1.6236
 
1.7%
1.828
 
1.3%
1.7219
 
0.9%
1.6317
 
0.8%
1.6716
 
0.8%
1.7815
 
0.7%
Other values (1564)1788
84.7%
ValueCountFrequency (%)
1.451
 
< 0.1%
1.4563461
 
< 0.1%
1.481
 
< 0.1%
1.4816821
 
< 0.1%
1.4832841
 
< 0.1%
1.4864841
 
< 0.1%
1.4894091
 
< 0.1%
1.4914411
 
< 0.1%
1.4985611
 
< 0.1%
1.513
0.6%
ValueCountFrequency (%)
1.981
< 0.1%
1.9756631
< 0.1%
1.9474061
< 0.1%
1.9427251
< 0.1%
1.9312631
< 0.1%
1.9304161
< 0.1%
1.932
0.1%
1.921
< 0.1%
1.9195431
< 0.1%
1.9188591
< 0.1%

Weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1525
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.58605813
Minimum39
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2023-05-26T10:01:07.687233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile48.5
Q165.473343
median83
Q3107.430682
95-th percentile131.9161515
Maximum173
Range134
Interquartile range (IQR)41.957339

Descriptive statistics

Standard deviation26.19117175
Coefficient of variation (CV)0.3024871707
Kurtosis-0.6998981576
Mean86.58605813
Median Absolute Deviation (MAD)21.735215
Skewness0.2554104954
Sum182783.1687
Variance685.9774774
MonotonicityNot monotonic
2023-05-26T10:01:07.984087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8059
 
2.8%
7043
 
2.0%
5042
 
2.0%
7540
 
1.9%
6037
 
1.8%
6526
 
1.2%
4222
 
1.0%
9020
 
0.9%
7819
 
0.9%
4518
 
0.9%
Other values (1515)1785
84.6%
ValueCountFrequency (%)
391
< 0.1%
39.1018051
< 0.1%
39.3715231
< 0.1%
39.6952951
< 0.1%
39.8501371
< 0.1%
401
< 0.1%
40.2027731
< 0.1%
40.3434631
< 0.1%
41.2201751
< 0.1%
41.2685971
< 0.1%
ValueCountFrequency (%)
1731
< 0.1%
165.0572691
< 0.1%
160.9353511
< 0.1%
160.6394051
< 0.1%
155.8720931
< 0.1%
155.2426721
< 0.1%
154.6184461
< 0.1%
153.9599451
< 0.1%
153.1494911
< 0.1%
152.7205451
< 0.1%

family_history_with_overweight
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
True
1726 
False
385 
ValueCountFrequency (%)
True1726
81.8%
False385
 
18.2%
2023-05-26T10:01:08.299162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

FAVC
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
True
1866 
False
245 
ValueCountFrequency (%)
True1866
88.4%
False245
 
11.6%
2023-05-26T10:01:08.535767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

FCVC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct810
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.419043062
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2023-05-26T10:01:08.785788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5232145
Q12
median2.385502
Q33
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5339265785
Coefficient of variation (CV)0.2207180959
Kurtosis-0.637545902
Mean2.419043062
Median Absolute Deviation (MAD)0.385502
Skewness-0.4329058314
Sum5106.599903
Variance0.2850775912
MonotonicityNot monotonic
2023-05-26T10:01:09.069487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3652
30.9%
2600
28.4%
133
 
1.6%
2.8231792
 
0.1%
2.214982
 
0.1%
2.7950862
 
0.1%
2.4425362
 
0.1%
2.816462
 
0.1%
2.9380312
 
0.1%
2.9549962
 
0.1%
Other values (800)812
38.5%
ValueCountFrequency (%)
133
1.6%
1.0035661
 
< 0.1%
1.0055781
 
< 0.1%
1.008761
 
< 0.1%
1.0311491
 
< 0.1%
1.0361591
 
< 0.1%
1.0364141
 
< 0.1%
1.0526991
 
< 0.1%
1.0535341
 
< 0.1%
1.0634491
 
< 0.1%
ValueCountFrequency (%)
3652
30.9%
2.9984411
 
< 0.1%
2.9979511
 
< 0.1%
2.9975241
 
< 0.1%
2.9967171
 
< 0.1%
2.9961861
 
< 0.1%
2.9955991
 
< 0.1%
2.994481
 
< 0.1%
2.9923291
 
< 0.1%
2.9922051
 
< 0.1%

NCP
Real number (ℝ≥0)

Distinct635
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.68562805
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2023-05-26T10:01:09.387717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.658738
median3
Q33
95-th percentile3.750881
Maximum4
Range3
Interquartile range (IQR)0.341262

Descriptive statistics

Standard deviation0.7780386488
Coefficient of variation (CV)0.2897045438
Kurtosis0.3855266238
Mean2.68562805
Median Absolute Deviation (MAD)0
Skewness-1.107097267
Sum5669.360813
Variance0.6053441391
MonotonicityNot monotonic
2023-05-26T10:01:09.663405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31203
57.0%
1199
 
9.4%
469
 
3.3%
2.776842
 
0.1%
3.9854422
 
0.1%
1.737622
 
0.1%
1.8943842
 
0.1%
1.1046422
 
0.1%
2.6446922
 
0.1%
3.5598412
 
0.1%
Other values (625)626
29.7%
ValueCountFrequency (%)
1199
9.4%
1.0002831
 
< 0.1%
1.0004141
 
< 0.1%
1.000611
 
< 0.1%
1.0013831
 
< 0.1%
1.0015421
 
< 0.1%
1.0016331
 
< 0.1%
1.0053911
 
< 0.1%
1.0094261
 
< 0.1%
1.0103191
 
< 0.1%
ValueCountFrequency (%)
469
3.3%
3.9995911
 
< 0.1%
3.9987661
 
< 0.1%
3.9986181
 
< 0.1%
3.9959571
 
< 0.1%
3.9951471
 
< 0.1%
3.9945881
 
< 0.1%
3.9909251
 
< 0.1%
3.989551
 
< 0.1%
3.9894921
 
< 0.1%

CAEC
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Sometimes
1765 
Frequently
242 
Always
 
53
no
 
51

Length

Max length10
Median length9
Mean length8.870203695
Min length2

Characters and Unicode

Total characters18725
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowSometimes
3rd rowSometimes
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes1765
83.6%
Frequently242
 
11.5%
Always53
 
2.5%
no51
 
2.4%

Length

2023-05-26T10:01:09.979955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-05-26T10:01:10.287430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
sometimes1765
83.6%
frequently242
 
11.5%
always53
 
2.5%
no51
 
2.4%

Most occurring characters

ValueCountFrequency (%)
e4014
21.4%
m3530
18.9%
t2007
10.7%
s1818
9.7%
o1816
9.7%
S1765
9.4%
i1765
9.4%
y295
 
1.6%
l295
 
1.6%
n293
 
1.6%
Other values (7)1127
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16665
89.0%
Uppercase Letter2060
 
11.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4014
24.1%
m3530
21.2%
t2007
12.0%
s1818
10.9%
o1816
10.9%
i1765
10.6%
y295
 
1.8%
l295
 
1.8%
n293
 
1.8%
r242
 
1.5%
Other values (4)590
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
S1765
85.7%
F242
 
11.7%
A53
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin18725
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4014
21.4%
m3530
18.9%
t2007
10.7%
s1818
9.7%
o1816
9.7%
S1765
9.4%
i1765
9.4%
y295
 
1.6%
l295
 
1.6%
n293
 
1.6%
Other values (7)1127
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII18725
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4014
21.4%
m3530
18.9%
t2007
10.7%
s1818
9.7%
o1816
9.7%
S1765
9.4%
i1765
9.4%
y295
 
1.6%
l295
 
1.6%
n293
 
1.6%
Other values (7)1127
 
6.0%

SMOKE
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
False
2067 
True
 
44
ValueCountFrequency (%)
False2067
97.9%
True44
 
2.1%
2023-05-26T10:01:10.558431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

CH2O
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1268
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.008011404
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2023-05-26T10:01:10.798855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5848125
median2
Q32.47742
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)0.8926075

Descriptive statistics

Standard deviation0.6129534518
Coefficient of variation (CV)0.3052539695
Kurtosis-0.8793946101
Mean2.008011404
Median Absolute Deviation (MAD)0.452986
Skewness-0.1049116449
Sum4238.912074
Variance0.3757119341
MonotonicityNot monotonic
2023-05-26T10:01:11.081251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2448
 
21.2%
1211
 
10.0%
3162
 
7.7%
2.8256293
 
0.1%
1.6363263
 
0.1%
2.1159672
 
0.1%
2.1742482
 
0.1%
2.5300352
 
0.1%
2.4500692
 
0.1%
1.4399622
 
0.1%
Other values (1258)1274
60.4%
ValueCountFrequency (%)
1211
10.0%
1.0004631
 
< 0.1%
1.0005361
 
< 0.1%
1.0005441
 
< 0.1%
1.0006951
 
< 0.1%
1.0013071
 
< 0.1%
1.0019951
 
< 0.1%
1.0022921
 
< 0.1%
1.0030631
 
< 0.1%
1.0035631
 
< 0.1%
ValueCountFrequency (%)
3162
7.7%
2.9994951
 
< 0.1%
2.9945151
 
< 0.1%
2.9934481
 
< 0.1%
2.9916711
 
< 0.1%
2.9893891
 
< 0.1%
2.9887711
 
< 0.1%
2.9877181
 
< 0.1%
2.9874061
 
< 0.1%
2.9843231
 
< 0.1%

SCC
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
False
2015 
True
 
96
ValueCountFrequency (%)
False2015
95.5%
True96
 
4.5%
2023-05-26T10:01:11.392089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

FAF
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1190
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.010297696
Minimum0
Maximum3
Zeros411
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2023-05-26T10:01:11.633276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.124505
median1
Q31.6666775
95-th percentile2.677133
Maximum3
Range3
Interquartile range (IQR)1.5421725

Descriptive statistics

Standard deviation0.8505924308
Coefficient of variation (CV)0.8419225683
Kurtosis-0.6205877595
Mean1.010297696
Median Absolute Deviation (MAD)0.804157
Skewness0.4984896147
Sum2132.738436
Variance0.7235074834
MonotonicityNot monotonic
2023-05-26T10:01:11.892596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0411
 
19.5%
1234
 
11.1%
2183
 
8.7%
375
 
3.6%
0.1101742
 
0.1%
1.6615562
 
0.1%
0.2453542
 
0.1%
1.0678172
 
0.1%
0.2880322
 
0.1%
1.2524722
 
0.1%
Other values (1180)1196
56.7%
ValueCountFrequency (%)
0411
19.5%
9.6 × 10-51
 
< 0.1%
0.0002721
 
< 0.1%
0.0004541
 
< 0.1%
0.0010151
 
< 0.1%
0.0010861
 
< 0.1%
0.0012721
 
< 0.1%
0.0012971
 
< 0.1%
0.002031
 
< 0.1%
0.003421
 
< 0.1%
ValueCountFrequency (%)
375
3.6%
2.9999181
 
< 0.1%
2.9989811
 
< 0.1%
2.9718321
 
< 0.1%
2.9397331
 
< 0.1%
2.9365511
 
< 0.1%
2.9315271
 
< 0.1%
2.8929222
 
0.1%
2.8919861
 
< 0.1%
2.891181
 
< 0.1%

TUE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1129
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6578659237
Minimum0
Maximum2
Zeros557
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2023-05-26T10:01:12.211385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.62535
Q31
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6089272597
Coefficient of variation (CV)0.9256099727
Kurtosis-0.5486604004
Mean0.6578659237
Median Absolute Deviation (MAD)0.484872
Skewness0.6185024143
Sum1388.754965
Variance0.3707924076
MonotonicityNot monotonic
2023-05-26T10:01:12.544369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0557
26.4%
1292
 
13.8%
2109
 
5.2%
0.6308664
 
0.2%
1.1198773
 
0.1%
0.00263
 
0.1%
0.0092542
 
0.1%
0.83242
 
0.1%
1.365952
 
0.1%
0.8285492
 
0.1%
Other values (1119)1135
53.8%
ValueCountFrequency (%)
0557
26.4%
7.3 × 10-51
 
< 0.1%
0.0003551
 
< 0.1%
0.0004361
 
< 0.1%
0.0010961
 
< 0.1%
0.001331
 
< 0.1%
0.0013371
 
< 0.1%
0.0015181
 
< 0.1%
0.001591
 
< 0.1%
0.001641
 
< 0.1%
ValueCountFrequency (%)
2109
5.2%
1.992191
 
< 0.1%
1.9906171
 
< 0.1%
1.9836781
 
< 0.1%
1.9808751
 
< 0.1%
1.9780431
 
< 0.1%
1.9729261
 
< 0.1%
1.971171
 
< 0.1%
1.9695071
 
< 0.1%
1.9672591
 
< 0.1%

CALC
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Sometimes
1401 
no
639 
Frequently
 
70
Always
 
1

Length

Max length10
Median length9
Mean length6.912837518
Min length2

Characters and Unicode

Total characters14593
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowno
2nd rowSometimes
3rd rowFrequently
4th rowFrequently
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes1401
66.4%
no639
30.3%
Frequently70
 
3.3%
Always1
 
< 0.1%

Length

2023-05-26T10:01:12.855986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-05-26T10:01:13.148805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
sometimes1401
66.4%
no639
30.3%
frequently70
 
3.3%
always1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e2942
20.2%
m2802
19.2%
o2040
14.0%
t1471
10.1%
s1402
9.6%
S1401
9.6%
i1401
9.6%
n709
 
4.9%
y71
 
0.5%
l71
 
0.5%
Other values (7)283
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13121
89.9%
Uppercase Letter1472
 
10.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2942
22.4%
m2802
21.4%
o2040
15.5%
t1471
11.2%
s1402
10.7%
i1401
10.7%
n709
 
5.4%
y71
 
0.5%
l71
 
0.5%
u70
 
0.5%
Other values (4)142
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
S1401
95.2%
F70
 
4.8%
A1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin14593
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2942
20.2%
m2802
19.2%
o2040
14.0%
t1471
10.1%
s1402
9.6%
S1401
9.6%
i1401
9.6%
n709
 
4.9%
y71
 
0.5%
l71
 
0.5%
Other values (7)283
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII14593
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2942
20.2%
m2802
19.2%
o2040
14.0%
t1471
10.1%
s1402
9.6%
S1401
9.6%
i1401
9.6%
n709
 
4.9%
y71
 
0.5%
l71
 
0.5%
Other values (7)283
 
1.9%

MTRANS
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Public_Transportation
1580 
Automobile
457 
Walking
 
56
Motorbike
 
11
Bike
 
7

Length

Max length21
Median length21
Mean length18.12837518
Min length4

Characters and Unicode

Total characters38269
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublic_Transportation
2nd rowPublic_Transportation
3rd rowPublic_Transportation
4th rowWalking
5th rowPublic_Transportation

Common Values

ValueCountFrequency (%)
Public_Transportation1580
74.8%
Automobile457
 
21.6%
Walking56
 
2.7%
Motorbike11
 
0.5%
Bike7
 
0.3%

Length

2023-05-26T10:01:13.426166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-05-26T10:01:13.731596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
public_transportation1580
74.8%
automobile457
 
21.6%
walking56
 
2.7%
motorbike11
 
0.5%
bike7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o4096
10.7%
i3691
 
9.6%
t3628
 
9.5%
a3216
 
8.4%
n3216
 
8.4%
r3171
 
8.3%
l2093
 
5.5%
b2048
 
5.4%
u2037
 
5.3%
P1580
 
4.1%
Other values (13)9493
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32998
86.2%
Uppercase Letter3691
 
9.6%
Connector Punctuation1580
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o4096
12.4%
i3691
11.2%
t3628
11.0%
a3216
9.7%
n3216
9.7%
r3171
9.6%
l2093
6.3%
b2048
6.2%
u2037
6.2%
p1580
 
4.8%
Other values (6)4222
12.8%
Uppercase Letter
ValueCountFrequency (%)
P1580
42.8%
T1580
42.8%
A457
 
12.4%
W56
 
1.5%
M11
 
0.3%
B7
 
0.2%
Connector Punctuation
ValueCountFrequency (%)
_1580
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin36689
95.9%
Common1580
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o4096
11.2%
i3691
10.1%
t3628
9.9%
a3216
8.8%
n3216
8.8%
r3171
8.6%
l2093
 
5.7%
b2048
 
5.6%
u2037
 
5.6%
P1580
 
4.3%
Other values (12)7913
21.6%
Common
ValueCountFrequency (%)
_1580
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII38269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o4096
10.7%
i3691
 
9.6%
t3628
 
9.5%
a3216
 
8.4%
n3216
 
8.4%
r3171
 
8.3%
l2093
 
5.5%
b2048
 
5.4%
u2037
 
5.3%
P1580
 
4.1%
Other values (13)9493
24.8%

NObeyesdad
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Obesity_Type_I
351 
Obesity_Type_III
324 
Obesity_Type_II
297 
Overweight_Level_I
290 
Overweight_Level_II
290 
Other values (2)
559 

Length

Max length19
Median length16
Mean length16.19232591
Min length13

Characters and Unicode

Total characters34182
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal_Weight
2nd rowNormal_Weight
3rd rowNormal_Weight
4th rowOverweight_Level_I
5th rowOverweight_Level_II

Common Values

ValueCountFrequency (%)
Obesity_Type_I351
16.6%
Obesity_Type_III324
15.3%
Obesity_Type_II297
14.1%
Overweight_Level_I290
13.7%
Overweight_Level_II290
13.7%
Normal_Weight287
13.6%
Insufficient_Weight272
12.9%

Length

2023-05-26T10:01:14.055080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-05-26T10:01:14.398661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
obesity_type_i351
16.6%
obesity_type_iii324
15.3%
obesity_type_ii297
14.1%
overweight_level_i290
13.7%
overweight_level_ii290
13.7%
normal_weight287
13.6%
insufficient_weight272
12.9%

Most occurring characters

ValueCountFrequency (%)
e5095
14.9%
_3663
 
10.7%
I3059
 
8.9%
i2655
 
7.8%
t2383
 
7.0%
y1944
 
5.7%
O1552
 
4.5%
s1244
 
3.6%
v1160
 
3.4%
g1139
 
3.3%
Other values (17)10288
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23510
68.8%
Uppercase Letter7009
 
20.5%
Connector Punctuation3663
 
10.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5095
21.7%
i2655
11.3%
t2383
10.1%
y1944
 
8.3%
s1244
 
5.3%
v1160
 
4.9%
g1139
 
4.8%
h1139
 
4.8%
p972
 
4.1%
b972
 
4.1%
Other values (10)4807
20.4%
Uppercase Letter
ValueCountFrequency (%)
I3059
43.6%
O1552
22.1%
T972
 
13.9%
L580
 
8.3%
W559
 
8.0%
N287
 
4.1%
Connector Punctuation
ValueCountFrequency (%)
_3663
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin30519
89.3%
Common3663
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5095
16.7%
I3059
 
10.0%
i2655
 
8.7%
t2383
 
7.8%
y1944
 
6.4%
O1552
 
5.1%
s1244
 
4.1%
v1160
 
3.8%
g1139
 
3.7%
h1139
 
3.7%
Other values (16)9149
30.0%
Common
ValueCountFrequency (%)
_3663
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII34182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e5095
14.9%
_3663
 
10.7%
I3059
 
8.9%
i2655
 
7.8%
t2383
 
7.0%
y1944
 
5.7%
O1552
 
4.5%
s1244
 
3.6%
v1160
 
3.4%
g1139
 
3.3%
Other values (17)10288
30.1%

Interactions

2023-05-26T10:01:02.628078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:46.888802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:48.977542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:51.098382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:53.251063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:55.555745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:57.761753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:00.092054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:02.890877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:47.143009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:49.218227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:51.341263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:53.503781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:55.816729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:58.040361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:00.333849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:03.170995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:47.419725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:49.473274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:51.594740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:53.778183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:56.095820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:58.317929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:00.601238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:03.452973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:47.680303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:49.726804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:51.858505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:54.060620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:56.356212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:58.602344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:00.875149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:03.757134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:47.946461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:50.005311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:52.143118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:54.370353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:56.651491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:58.909370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:01.153942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:04.041023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:48.193953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:50.270692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:52.408897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:54.663084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:56.931657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:59.187729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:01.771558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:04.354629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:48.469434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:50.543122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:52.701849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:54.961756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:57.225226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:59.493000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:02.065355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:04.621893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:48.716354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:50.822643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:52.960624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:55.245955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:57.471899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:00:59.783334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-26T10:01:02.338010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2023-05-26T10:01:14.715099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman\'s ρ

The Spearman\'s rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson\'s r. It\'s value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-05-26T10:01:15.504675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson\'s r

The Pearson\'s correlation coefficient (r) is a measure of linear correlation between two variables. It\'s value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-05-26T10:01:15.865964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall\'s τ

Similarly to Spearman\'s rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It\'s value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-05-26T10:01:16.244084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér\'s V (φc)

Cramér\'s V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér\'s V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-05-26T10:01:16.631593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-05-26T10:01:05.139393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-26T10:01:05.791550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

GenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSNObeyesdad
0Female21.01.6264.0yesno2.03.0Sometimesno2.0no0.01.0noPublic_TransportationNormal_Weight
1Female21.01.5256.0yesno3.03.0Sometimesyes3.0yes3.00.0SometimesPublic_TransportationNormal_Weight
2Male23.01.8077.0yesno2.03.0Sometimesno2.0no2.01.0FrequentlyPublic_TransportationNormal_Weight
3Male27.01.8087.0nono3.03.0Sometimesno2.0no2.00.0FrequentlyWalkingOverweight_Level_I
4Male22.01.7889.8nono2.01.0Sometimesno2.0no0.00.0SometimesPublic_TransportationOverweight_Level_II
5Male29.01.6253.0noyes2.03.0Sometimesno2.0no0.00.0SometimesAutomobileNormal_Weight
6Female23.01.5055.0yesyes3.03.0Sometimesno2.0no1.00.0SometimesMotorbikeNormal_Weight
7Male22.01.6453.0nono2.03.0Sometimesno2.0no3.00.0SometimesPublic_TransportationNormal_Weight
8Male24.01.7864.0yesyes3.03.0Sometimesno2.0no1.01.0FrequentlyPublic_TransportationNormal_Weight
9Male22.01.7268.0yesyes2.03.0Sometimesno2.0no1.01.0noPublic_TransportationNormal_Weight

Last rows

GenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSNObeyesdad
2101Female25.7220041.628470107.218949yesyes3.03.0Sometimesno2.487070no0.0673290.455823SometimesPublic_TransportationObesity_Type_III
2102Female25.7656281.627839108.107360yesyes3.03.0Sometimesno2.320068no0.0452460.413106SometimesPublic_TransportationObesity_Type_III
2103Female21.0168491.724268133.033523yesyes3.03.0Sometimesno1.650612no1.5376390.912457SometimesPublic_TransportationObesity_Type_III
2104Female21.6823671.732383133.043941yesyes3.03.0Sometimesno1.610768no1.5103980.931455SometimesPublic_TransportationObesity_Type_III
2105Female21.2859651.726920131.335786yesyes3.03.0Sometimesno1.796267no1.7283320.897924SometimesPublic_TransportationObesity_Type_III
2106Female20.9768421.710730131.408528yesyes3.03.0Sometimesno1.728139no1.6762690.906247SometimesPublic_TransportationObesity_Type_III
2107Female21.9829421.748584133.742943yesyes3.03.0Sometimesno2.005130no1.3413900.599270SometimesPublic_TransportationObesity_Type_III
2108Female22.5240361.752206133.689352yesyes3.03.0Sometimesno2.054193no1.4142090.646288SometimesPublic_TransportationObesity_Type_III
2109Female24.3619361.739450133.346641yesyes3.03.0Sometimesno2.852339no1.1391070.586035SometimesPublic_TransportationObesity_Type_III
2110Female23.6647091.738836133.472641yesyes3.03.0Sometimesno2.863513no1.0264520.714137SometimesPublic_TransportationObesity_Type_III

Duplicate rows

Most frequently occurring

GenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSNObeyesdad# duplicates
7Male21.01.6270.0noyes2.01.0nono3.0no1.00.0SometimesPublic_TransportationOverweight_Level_I15
3Female21.01.5242.0noyes3.01.0Frequentlyno1.0no0.00.0SometimesPublic_TransportationInsufficient_Weight4
0Female16.01.6658.0nono2.01.0Sometimesno1.0no0.01.0noWalkingNormal_Weight2
1Female18.01.6255.0yesyes2.03.0Frequentlyno1.0no1.01.0noPublic_TransportationNormal_Weight2
2Female21.01.5242.0nono3.01.0Frequentlyno1.0no0.00.0SometimesPublic_TransportationInsufficient_Weight2
4Female22.01.6965.0yesyes2.03.0Sometimesno2.0no1.01.0SometimesPublic_TransportationNormal_Weight2
5Female25.01.5755.0noyes2.01.0Sometimesno2.0no2.00.0SometimesPublic_TransportationNormal_Weight2
6Male18.01.7253.0yesyes2.03.0Sometimesno2.0no0.02.0SometimesPublic_TransportationInsufficient_Weight2
8Male22.01.7475.0yesyes3.03.0Frequentlyno1.0no1.00.0noAutomobileNormal_Weight2
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